Effect of angiotensin-converting enzyme inhibitor and angiotensin receptor blocker initiation on organ support-free days in patients hospitalized with COVID-19

IMPORTANCE Overactivation of the renin-angiotensin system (RAS) may contribute to poor clinical outcomes in patients with COVID-19. Objective To determine whether angiotensin-converting enzyme (ACE) inhibitor or angiotensin receptor blocker (ARB) initiation improves outcomes in patients hospitalized for COVID-19. DESIGN, SETTING, AND PARTICIPANTS In an ongoing, adaptive platform randomized clinical trial, 721 critically ill and 58 non–critically ill hospitalized adults were randomized to receive an RAS inhibitor or control between March 16, 2021, and February 25, 2022, at 69 sites in 7 countries (final follow-up on June 1, 2022). INTERVENTIONS Patients were randomized to receive open-label initiation of an ACE inhibitor (n = 257), ARB (n = 248), ARB in combination with DMX-200 (a chemokine receptor-2 inhibitor; n = 10), or no RAS inhibitor (control; n = 264) for up to 10 days. MAIN OUTCOMES AND MEASURES The primary outcome was organ support–free days, a composite of hospital survival and days alive without cardiovascular or respiratory organ support through 21 days. The primary analysis was a bayesian cumulative logistic model. Odds ratios (ORs) greater than 1 represent improved outcomes. RESULTS On February 25, 2022, enrollment was discontinued due to safety concerns. Among 679 critically ill patients with available primary outcome data, the median age was 56 years and 239 participants (35.2%) were women. Median (IQR) organ support–free days among critically ill patients was 10 (–1 to 16) in the ACE inhibitor group (n = 231), 8 (–1 to 17) in the ARB group (n = 217), and 12 (0 to 17) in the control group (n = 231) (median adjusted odds ratios of 0.77 [95% bayesian credible interval, 0.58-1.06] for improvement for ACE inhibitor and 0.76 [95% credible interval, 0.56-1.05] for ARB compared with control). The posterior probabilities that ACE inhibitors and ARBs worsened organ support–free days compared with control were 94.9% and 95.4%, respectively. Hospital survival occurred in 166 of 231 critically ill participants (71.9%) in the ACE inhibitor group, 152 of 217 (70.0%) in the ARB group, and 182 of 231 (78.8%) in the control group (posterior probabilities that ACE inhibitor and ARB worsened hospital survival compared with control were 95.3% and 98.1%, respectively). CONCLUSIONS AND RELEVANCE In this trial, among critically ill adults with COVID-19, initiation of an ACE inhibitor or ARB did not improve, and likely worsened, clinical outcomes. TRIAL REGISTRATION ClinicalTrials.gov Identifier: NCT0273570

Identification of Hidden Key Hepatitis C Populations: An Evaluation of Screening Practices Using Mixed Epidemiological Methods

BACKGROUND: Hepatitis C virus (HCV) is a major cause of liver diseases worldwide. Due to its asymptomatic nature, screening is necessary for identification. Because screening of the total population is not cost effective, it is important to identify which risk factors for positivity characterize the key populations in which targeting of screening yields the highest numbers of HCV positives, and assess which of these key populations have remained hidden to current care. METHODS: Laboratory registry data (2002-2008) were retrieved for all HCV tests (23,800) in the south of the Netherlands (adult population 500,000). Screening trends were tested using Poisson regression and chi-square tests. Risk factors for HCV positivity were assessed using a logistic regression. The hidden HCV-positive population was estimated by a capture-recapture approach. RESULTS: The number of tests increased over time (2,388 to 4,149, p<.01). Nevertheless, the positivity rate among those screened decreased between 2002 and 2008 (6.3% to 2.1%, p<.01). The population prevalence was estimated to be 0.49% (95%CI 0.41-0.59). Of all HCV-positive patients, 66% were hidden to current screening practices. Risk factors associated with positivity were low socio-economic status, male sex, and age between 36-55. In future screening 48% (95%CI 37-63) of total patients and 47% (95%CI 32-70) of hidden patients can be identified by targeting 9% (men with low socio-economic status, between 36-55 years old) of the total population. CONCLUSIONS: Although the current HCV screening policy increasingly addresses high-risk populations, it only reaches one third of positive patients. This study shows that combining easily identifiable demographic risk factors can be used to identify key populations as a likely target for effective HCV screening. We recommend strengthening screening among middle-aged man, living in low socio-economic neighborhoods

Distribution of HCV cases from the three care providers and the estimated hidden population.

<p>Laboratory surveillance data, South Limburg, the Netherlands, 2002–2008.</p

Factors of HCV testing (GLM, Poisson) and factors of HCV positivity (logistic regression).

<p>Laboratory surveillance data, South Limburg, the Netherlands, 2002–2008.</p>a<p>Adjusted for sex, age, care provider, test year, SES by percentage of people on social welfare, and SES by mean property value. * <i>p</i><0.05 **<i>p</i><0.01.</p>b<p>Non-hospital, non-GP test providers.</p><p>GP: general practitioner; HCV: hepatitis C virus; RR: Relative Risk; OR: Odds Ratio; SES: Socio-economic status.</p

The number of observed and hidden cases and prevalences in the total and non-screened population.

<p>Laboratory surveillance data, South Limburg, the Netherlands, 2002–2008.</p>a<p>Based on population statistics from 2007 <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0051194#pone.0051194-StatisticsNetherlands2" target="_blank">[27]</a>.</p>b<p>Age at first positive test.</p><p>HCV: hepatitis C virus.</p><p>SESsw: Socio-economic status based on % of people receiving social welfare.</p><p>SESpv: Socio-economic status based on property value.</p

The total number of HCV tests, the positivity rate and the population test rate.

<p>Laboratory surveillance data, South Limburg, the Netherlands, 2002–2008.</p

Screening scenarios: The percentage of the total population screened, which percentage of the (hidden) total HCV patients will be detected, and the number of people needed to screen.

<p>Laboratory surveillance data, South Limburg, the Netherlands, 2002–2008.</p>a<p>Based on population statistics from 2007 <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0051194#pone.0051194-StatisticsNetherlands2" target="_blank">[27]</a>.</p>b<p>Age at first positive test.</p><p>SESsw: Socio-economic status based on % of people receiving social welfare.</p><p>SESpv: Socio-economic status based on property value.</p